The DOI matches its Crossref record; 91 of the 91 references it cites were found in a registry or an index.
Identification of mobile genetic elements with geNomad
Open accessAbstract
Identifying and characterizing mobile genetic elements in sequencing data is essential for understanding their diversity, ecology, biotechnological applications and impact on public health. Here we introduce geNomad, a classification and annotation framework that combines information from gene content and a deep neural network to identify sequences of plasmids and viruses. geNomad uses a dataset of more than 200,000 marker protein profiles to provide functional gene annotation and taxonomic assignment of viral genomes. Using a conditional random field model, geNomad also detects proviruses integrated into host genomes with high precision. In benchmarks, geNomad achieved high classification performance for diverse plasmids and viruses (Matthews correlation coefficient of 77.8% and 95.3%, respectively), substantially outperforming other tools. Leveraging geNomad’s speed and scalability, we processed over 2.7 trillion base pairs of sequencing data, leading to the discovery of millions of viruses and plasmids that are available through the IMG/VR and IMG/PR databases. geNomad is available at https://portal.nersc.gov/genomad .
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XGBoost: A Scalable Tree Boosting System
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IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era
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Pfam: The protein families database in 2021
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Bridge RNAs direct programmable recombination of target and donor DNA
Durrant, M. G., Perry, N. T., Pai, J. J., Jangid, A. R., Athukoralage, J. S., Hiraizumi, M., McSpedon, J. P., Pawluk, A., Nishimasu, H., Konermann, S., et al. (2024) · Nature · doi:10.1038/s41586-024-07552-4 · Cited by 163
IMG/PR: a database of plasmids from genomes and metagenomes with rich annotations and metadata
Camargo, A. P., Call, L., Roux, S., Nayfach, S., Huntemann, M., Palaniappan, K., Ratner, A., Chu, K., Mukherjeep, S., Reddy, T. B. K., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad964 · Cited by 87
Self-citationLarge language models improve annotation of prokaryotic viral proteins
Flamholz, Z. N., Biller, S. J., & Kelly, L. (2024) · Nature Microbiology · doi:10.1038/s41564-023-01584-8 · Cited by 92
Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences
Ruffolo, J. A., Nayfach, S., Gallagher, J., Bhatnagar, A., Beazer, J., Hussain, R., Russ, J., Yip, J., Hill, E., Pacesa, M., et al. (2024) · doi:10.1101/2024.04.22.590591 · Cited by 90
Self-citationProtein structure-informed bacteriophage genome annotation with Phold
Bouras, G., Grigson, S. R., Mirdita, M., Heinzinger, M., Papudeshi, B., Mallawaarachchi, V., Green, R., Kim, R. S., Mihalia, V., Psaltis, A. J., et al. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkaf1448 · Cited by 71
Metadata
- Authors
- Camargo, A. P., Roux, S., Schulz, F., Babinski, M., Xu, Y., Hu, B., Chain, P. S. G., Nayfach, S., & Kyrpides, N. C.
- Venue
- Nature Biotechnology
- Published
- 2023-09-21
- Volume
- 42
- Issue
- 8
- Pages
- 1303-1312
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W4386923826
- Cited by
- 980
- References
- 91
- FWCI
- 153.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
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